## Posts

Nov, 1

### CUDA-Lite: Reducing GPU programming complexity

Abstract. The computer industry has transitioned into multi-core and many-core parallel systems. The CUDA programming environment from NVIDIA is an attempt to make programming many-core GPUs more accessible to programmers. However, there are still many burdens placed upon the programmer to maximize performance when using CUDA. One such burden is dealing with the complex memory […]

Nov, 1

### Program optimization space pruning for a multithreaded gpu

Program optimization for highly-parallel systems has historically been considered an art, with experts doing much of the performance tuning by hand. With the introduction of inexpensive, single-chip, massively parallel platforms, more developers will be creating highly-parallel applications for these platforms, who lack the substantial experience and knowledge needed to maximize their performance. This creates a […]

Nov, 1

### Interactive, GPU-Based Level Sets for 3D Segmentation

While level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. This paper presents a tool for […]

Nov, 1

### High-throughput sequence alignment using Graphics Processing Units

BACKGROUND:The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for […]

Oct, 30

### Bio-sequence database scanning on a GPU

Protein sequences with unknown functionality are often compared to a set of known sequences to detect functional similarities. Efficient dynamic programming algorithms exist for this problem, however current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these […]

Oct, 30

### Graphics Processing Units and High-Dimensional Optimization

This paper discusses the potential of graphics processing units (GPUs) in high-dimensional optimization problems. A single GPU card with hundreds of arithmetic cores can be inserted in a personal computer and dramatically accelerates many statistical algorithms. To exploit these devices fully, optimization algorithms should reduce to multiple parallel tasks, each accessing a limited amount of […]

Oct, 30

### How GPUs Work

GPUs have moved away from the traditional fixed-function 3D graphics pipeline toward a flexible general-purpose computational engine. Today, GPUs can implement many parallel algorithms directly using graphics hardware. Well-suited algorithms that leverage all the underlying computational horsepower often achieve tremendous speedups. Truly, the GPU is the first widely deployed commodity desktop parallel computer

Oct, 30

### GPUs: A Closer Look

A gamer wanders through a virtual world rendered in near- cinematic detail. Seconds later, the screen fills with a 3D explosion, the result of unseen enemies hiding in physically accurate shadows. Disappointed, the user exits the game and returns to a computer desktop that exhibits the stylish 3D look-and-feel of a modern window manager. Both […]

Oct, 30

### Mapping computational concepts to GPUs

Recently, graphics processors have emerged as a powerful computational platform. A variety of encouraging results, mostly from researchers using GPUs to accelerate scientific computing and visualization applications, have shown that significant speedups can be achieved by applying GPUs to data-parallel computational problems. However, attaining these speedups requires knowledge of GPU programming and architecture.The preceding chapters […]

Oct, 30

### Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

Modern videogames place increasing demands on the computational and graphical hardware, leading to novel architectures that have great potential in the context of high performance computing and molecular simulation. We demonstrate that Graphical Processing Units (GPUs) can be used very efficiently to calculate two-electron repulsion integrals over Gaussian basis functionsthe first step in most quantum […]

Oct, 30

### A Memory Model for Scientific Algorithms on Graphics Processors

We present a memory model to analyze and improve the performance of scientific algorithms on graphics processing units (GPUs). Our memory model is based on texturing hardware, which uses a 2D block-based array representation to perform the underlying computations. We incorporate many characteristics of GPU architectures including smaller cache sizes, 2D block representations, and use […]

Oct, 30

### Accelerating Density Functional Calculations with Graphics Processing Unit

An algorithm is presented for graphics processing units (GPUs), which execute single-precision arithmetic much faster than commodity microprocessors (CPUs), to calculate the exchange-correlation term in ab initio density functional calculations. The algorithm was implemented and applied to two molecules, taxol and valinomycin. The errors in the total energies were about 10−5 a.u., which is accurate […]